


from __future__ import annotations

import unittest

from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import (
           TF_MODEL_FOR_PRETRAINING_MAPPING,
           TFMobileBertForMaskedLM,
           TFMobileBertForMultipleChoice,
           TFMobileBertForNextSentencePrediction,
           TFMobileBertForPreTraining,
           TFMobileBertForQuestionAnswering,
           TFMobileBertForSequenceClassification,
           TFMobileBertForTokenClassification,
           TFMobileBertModel,
       )



@require_tf
class        A     (		lowerCamelCase__	, lowerCamelCase__	, unittest.TestCase	):







     '''simple docstring'''







     lowerCamelCase :						Dict            =   (
         (
             TFMobileBertModel,
             TFMobileBertForMaskedLM,
             TFMobileBertForNextSentencePrediction,
             TFMobileBertForPreTraining,
             TFMobileBertForQuestionAnswering,
             TFMobileBertForSequenceClassification,
             TFMobileBertForTokenClassification,
             TFMobileBertForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     lowerCamelCase :						str            =   (
         {
             """feature-extraction""": TFMobileBertModel,
             """fill-mask""": TFMobileBertForMaskedLM,
             """question-answering""": TFMobileBertForQuestionAnswering,
             """text-classification""": TFMobileBertForSequenceClassification,
             """token-classification""": TFMobileBertForTokenClassification,
             """zero-shot""": TFMobileBertForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     lowerCamelCase :						Dict            =   False
     lowerCamelCase :						int            =   False

     def 					UpperCAmelCase__       (				self    :							Optional[Any]			,				_UpperCamelCase    :							Union[str, Any]			,				_UpperCamelCase    :							Dict			,				_UpperCamelCase    :							Optional[Any]=False):
      _lowercase:       List[str]						=				super()._prepare_for_class(__snake_case			,				__snake_case			,				return_labels=__snake_case)

      if return_labels:
       if model_class in get_values(__snake_case):
        _lowercase:       int						=				tf.zeros(self.model_tester.batch_size			,				dtype=tf.intaa)

      return inputs_dict



     class        A     (		lowerCamelCase__	):







          '''simple docstring'''







          def __init__(				self    :							Any			,				_UpperCamelCase    :							Dict			,				_UpperCamelCase    :							str=13			,				_UpperCamelCase    :							str=7			,				_UpperCamelCase    :							Union[str, Any]=True			,				_UpperCamelCase    :							List[str]=True			,				_UpperCamelCase    :							Tuple=True			,				_UpperCamelCase    :							Any=True			,				_UpperCamelCase    :							Union[str, Any]=99			,				_UpperCamelCase    :							Dict=32			,				_UpperCamelCase    :							Any=32			,				_UpperCamelCase    :							str=2			,				_UpperCamelCase    :							Optional[Any]=4			,				_UpperCamelCase    :							int=37			,				_UpperCamelCase    :							str="gelu"			,				_UpperCamelCase    :							Any=0.1			,				_UpperCamelCase    :							Dict=0.1			,				_UpperCamelCase    :							Optional[int]=512			,				_UpperCamelCase    :							List[str]=16			,				_UpperCamelCase    :							str=2			,				_UpperCamelCase    :							Tuple=0.0_2			,				_UpperCamelCase    :							str=3			,				_UpperCamelCase    :							List[str]=4			,				_UpperCamelCase    :							Any=None			,				):
           _lowercase:       List[Any]						=				parent
           _lowercase:       int						=				batch_size
           _lowercase:       Optional[Any]						=				seq_length
           _lowercase:       int						=				is_training
           _lowercase:       List[Any]						=				use_input_mask
           _lowercase:       List[Any]						=				use_token_type_ids
           _lowercase:       Tuple						=				use_labels
           _lowercase:       Union[str, Any]						=				vocab_size
           _lowercase:       Tuple						=				hidden_size
           _lowercase:       int						=				num_hidden_layers
           _lowercase:       Union[str, Any]						=				num_attention_heads
           _lowercase:       List[Any]						=				intermediate_size
           _lowercase:       List[str]						=				hidden_act
           _lowercase:       Tuple						=				hidden_dropout_prob
           _lowercase:       Dict						=				attention_probs_dropout_prob
           _lowercase:       Dict						=				max_position_embeddings
           _lowercase:       Dict						=				type_vocab_size
           _lowercase:       List[str]						=				type_sequence_label_size
           _lowercase:       Union[str, Any]						=				initializer_range
           _lowercase:       str						=				num_labels
           _lowercase:       List[str]						=				num_choices
           _lowercase:       str						=				scope
           _lowercase:       Dict						=				embedding_size

          def 					UpperCAmelCase__       (				self    :							Tuple):
           _lowercase:       str						=				ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size)

           _lowercase:       int						=				None
           if self.use_input_mask:
            _lowercase:       List[str]						=				random_attention_mask([self.batch_size, self.seq_length])

           _lowercase:       Tuple						=				None
           if self.use_token_type_ids:
            _lowercase:       str						=				ids_tensor([self.batch_size, self.seq_length]			,				self.type_vocab_size)

           _lowercase:       str						=				None
           _lowercase:       Union[str, Any]						=				None
           _lowercase:       Union[str, Any]						=				None
           if self.use_labels:
            _lowercase:       Tuple						=				ids_tensor([self.batch_size]			,				self.type_sequence_label_size)
            _lowercase:       Dict						=				ids_tensor([self.batch_size, self.seq_length]			,				self.num_labels)
            _lowercase:       str						=				ids_tensor([self.batch_size]			,				self.num_choices)

           _lowercase:       Dict						=				MobileBertConfig(
               vocab_size=self.vocab_size			,				hidden_size=self.hidden_size			,				num_hidden_layers=self.num_hidden_layers			,				num_attention_heads=self.num_attention_heads			,				intermediate_size=self.intermediate_size			,				hidden_act=self.hidden_act			,				hidden_dropout_prob=self.hidden_dropout_prob			,				attention_probs_dropout_prob=self.attention_probs_dropout_prob			,				max_position_embeddings=self.max_position_embeddings			,				type_vocab_size=self.type_vocab_size			,				initializer_range=self.initializer_range			,				embedding_size=self.embedding_size			,				)

           return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

          def 					UpperCAmelCase__       (				self    :							str			,				_UpperCamelCase    :							Any			,				_UpperCamelCase    :							Any			,				_UpperCamelCase    :							Union[str, Any]			,				_UpperCamelCase    :							Any			,				_UpperCamelCase    :							int			,				_UpperCamelCase    :							Union[str, Any]			,				_UpperCamelCase    :							Tuple):
           _lowercase:       str						=				TFMobileBertModel(config=__snake_case)
           _lowercase:       Any						=				{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
           _lowercase:       List[Any]						=				model(__snake_case)

           _lowercase:       Union[str, Any]						=				[input_ids, input_mask]
           _lowercase:       List[Any]						=				model(__snake_case)

           _lowercase:       List[str]						=				model(__snake_case)

           self.parent.assertEqual(
               result.last_hidden_state.shape			,				(self.batch_size, self.seq_length, self.hidden_size))
           self.parent.assertEqual(result.pooler_output.shape			,				(self.batch_size, self.hidden_size))

          def 					UpperCAmelCase__       (				self    :							Union[str, Any]			,				_UpperCamelCase    :							Dict			,				_UpperCamelCase    :							Tuple			,				_UpperCamelCase    :							int			,				_UpperCamelCase    :							Any			,				_UpperCamelCase    :							List[Any]			,				_UpperCamelCase    :							int			,				_UpperCamelCase    :							Optional[int]):
           _lowercase:       Tuple						=				TFMobileBertForMaskedLM(config=__snake_case)
           _lowercase:       Any						=				{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
           _lowercase:       str						=				model(__snake_case)
           self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.seq_length, self.vocab_size))

          def 					UpperCAmelCase__       (				self    :							Dict			,				_UpperCamelCase    :							Dict			,				_UpperCamelCase    :							Any			,				_UpperCamelCase    :							List[str]			,				_UpperCamelCase    :							Any			,				_UpperCamelCase    :							List[Any]			,				_UpperCamelCase    :							Any			,				_UpperCamelCase    :							str):
           _lowercase:       int						=				TFMobileBertForNextSentencePrediction(config=__snake_case)
           _lowercase:       Dict						=				{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
           _lowercase:       List[str]						=				model(__snake_case)
           self.parent.assertEqual(result.logits.shape			,				(self.batch_size, 2))

          def 					UpperCAmelCase__       (				self    :							Optional[Any]			,				_UpperCamelCase    :							List[Any]			,				_UpperCamelCase    :							Dict			,				_UpperCamelCase    :							str			,				_UpperCamelCase    :							Union[str, Any]			,				_UpperCamelCase    :							List[str]			,				_UpperCamelCase    :							str			,				_UpperCamelCase    :							int):
           _lowercase:       str						=				TFMobileBertForPreTraining(config=__snake_case)
           _lowercase:       List[str]						=				{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
           _lowercase:       Tuple						=				model(__snake_case)
           self.parent.assertEqual(
               result.prediction_logits.shape			,				(self.batch_size, self.seq_length, self.vocab_size))
           self.parent.assertEqual(result.seq_relationship_logits.shape			,				(self.batch_size, 2))

          def 					UpperCAmelCase__       (				self    :							Optional[Any]			,				_UpperCamelCase    :							Any			,				_UpperCamelCase    :							int			,				_UpperCamelCase    :							Any			,				_UpperCamelCase    :							Union[str, Any]			,				_UpperCamelCase    :							Tuple			,				_UpperCamelCase    :							Dict			,				_UpperCamelCase    :							Dict):
           _lowercase:       List[Any]						=				self.num_labels
           _lowercase:       str						=				TFMobileBertForSequenceClassification(config=__snake_case)
           _lowercase:       Optional[Any]						=				{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
           _lowercase:       Dict						=				model(__snake_case)
           self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels))

          def 					UpperCAmelCase__       (				self    :							Any			,				_UpperCamelCase    :							Dict			,				_UpperCamelCase    :							Any			,				_UpperCamelCase    :							Tuple			,				_UpperCamelCase    :							str			,				_UpperCamelCase    :							Union[str, Any]			,				_UpperCamelCase    :							List[Any]			,				_UpperCamelCase    :							str):
           _lowercase:       Tuple						=				self.num_choices
           _lowercase:       Tuple						=				TFMobileBertForMultipleChoice(config=__snake_case)
           _lowercase:       List[str]						=				tf.tile(tf.expand_dims(__snake_case			,				1)			,				(1, self.num_choices, 1))
           _lowercase:       Union[str, Any]						=				tf.tile(tf.expand_dims(__snake_case			,				1)			,				(1, self.num_choices, 1))
           _lowercase:       int						=				tf.tile(tf.expand_dims(__snake_case			,				1)			,				(1, self.num_choices, 1))
           _lowercase:       str						=				{
               'input_ids': multiple_choice_inputs_ids,
               'attention_mask': multiple_choice_input_mask,
               'token_type_ids': multiple_choice_token_type_ids,
           }
           _lowercase:       str						=				model(__snake_case)
           self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_choices))

          def 					UpperCAmelCase__       (				self    :							Optional[int]			,				_UpperCamelCase    :							Dict			,				_UpperCamelCase    :							List[str]			,				_UpperCamelCase    :							Tuple			,				_UpperCamelCase    :							str			,				_UpperCamelCase    :							str			,				_UpperCamelCase    :							str			,				_UpperCamelCase    :							Optional[int]):
           _lowercase:       int						=				self.num_labels
           _lowercase:       Union[str, Any]						=				TFMobileBertForTokenClassification(config=__snake_case)
           _lowercase:       Tuple						=				{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
           _lowercase:       Union[str, Any]						=				model(__snake_case)
           self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.seq_length, self.num_labels))

          def 					UpperCAmelCase__       (				self    :							List[Any]			,				_UpperCamelCase    :							Union[str, Any]			,				_UpperCamelCase    :							Optional[int]			,				_UpperCamelCase    :							int			,				_UpperCamelCase    :							Union[str, Any]			,				_UpperCamelCase    :							Optional[int]			,				_UpperCamelCase    :							Union[str, Any]			,				_UpperCamelCase    :							Dict):
           _lowercase:       str						=				TFMobileBertForQuestionAnswering(config=__snake_case)
           _lowercase:       Any						=				{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
           _lowercase:       int						=				model(__snake_case)
           self.parent.assertEqual(result.start_logits.shape			,				(self.batch_size, self.seq_length))
           self.parent.assertEqual(result.end_logits.shape			,				(self.batch_size, self.seq_length))






          def 					UpperCAmelCase__       (				self    :							Union[str, Any]):
           _lowercase:       Optional[Any]						=				self.prepare_config_and_inputs()
           (
               _lowercase      
           ):       Dict						=				config_and_inputs
           _lowercase:       str						=				{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
           return config, inputs_dict






     def 					UpperCAmelCase__       (				self    :							str):
      _lowercase:       Any						=				TFMobileBertModelTest.TFMobileBertModelTester(self)
      _lowercase:       Union[str, Any]						=				ConfigTester(self			,				config_class=__snake_case			,				hidden_size=37)

     def 					UpperCAmelCase__       (				self    :							List[Any]):
      self.config_tester.run_common_tests()

     def 					UpperCAmelCase__       (				self    :							Optional[int]):
      _lowercase:       Dict						=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_model(*__snake_case)

     def 					UpperCAmelCase__       (				self    :							List[Any]):
      _lowercase:       Optional[Any]						=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_for_masked_lm(*__snake_case)

     def 					UpperCAmelCase__       (				self    :							Union[str, Any]):
      _lowercase:       int						=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__snake_case)

     def 					UpperCAmelCase__       (				self    :							str):
      _lowercase:       Optional[int]						=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__snake_case)

     def 					UpperCAmelCase__       (				self    :							int):
      _lowercase:       Any						=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_for_pretraining(*__snake_case)

     def 					UpperCAmelCase__       (				self    :							Any):
      _lowercase:       Dict						=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_for_question_answering(*__snake_case)

     def 					UpperCAmelCase__       (				self    :							str):
      _lowercase:       Tuple						=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__snake_case)

     def 					UpperCAmelCase__       (				self    :							List[str]):
      _lowercase:       List[Any]						=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_for_token_classification(*__snake_case)






     @slow
     def 					UpperCAmelCase__       (				self    :							Optional[int]):
      # for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      for model_name in ["google/mobilebert-uncased"]:
       _lowercase:       Dict						=				TFMobileBertModel.from_pretrained(__snake_case)
       self.assertIsNotNone(__snake_case)






@require_tf
class        A     (		unittest.TestCase	):







     '''simple docstring'''







     @slow
     def 					UpperCAmelCase__       (				self    :							Any):
      _lowercase:       str						=				TFMobileBertForPreTraining.from_pretrained("google/mobilebert-uncased")
      _lowercase:       List[Any]						=				tf.constant([[0, 1, 2, 3, 4, 5]])
      _lowercase:       List[Any]						=				model(__snake_case)[0]

      _lowercase:       Any						=				[1, 6, 30_522]
      self.assertEqual(output.shape			,				__snake_case)

      _lowercase:       Any						=				tf.constant(
          [
              [
                  [-4.5_9_1_9_5_4_7, -9.2_4_8_2_9_5, -9.6_4_5_2_5_6],
                  [-6.7_3_0_6_1_7_5, -6.4_4_0_2_8_4, -6.6_0_5_2_8_3_7],
                  [-7.2_7_4_3_5_0_6, -6.7_8_4_7_9_1_5, -6.0_2_4_6_7_3],
              ]
          ])
      tf.debugging.assert_near(output[:, :3, :3]			,				__snake_case			,				atol=1e-4)






'''simple docstring'''






import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 				a__(   lowerCamelCase__						,					unittest.TestCase      ):
    lowercase__					=       CodeGenTokenizer
    lowercase__					=       CodeGenTokenizerFast
    lowercase__					=       True
    lowercase__					=       {"""add_prefix_space""": True}
    lowercase__					=       False







    def      lowercase_			( self					:    List[str]      ):
      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      a		:   Any	  =	[
          'l',
          'o',
          'w',
          'e',
          'r',
          's',
          't',
          'i',
          'd',
          'n',
          '\u0120',
          '\u0120l',
          '\u0120n',
          '\u0120lo',
          '\u0120low',
          'er',
          '\u0120lowest',
          '\u0120newer',
          '\u0120wider',
          '<unk>',
          '<|endoftext|>',
      ]
      a		:   Dict	  =	dict(zip(__snake_case		,     range(len(__snake_case      )      )      )      )
      a		:   str	  =	['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
      a		:   List[str]	  =	{'unk_token': '<unk>'}

      a		:   List[str]	  =	os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES['vocab_file']      )
      a		:   Dict	  =	os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES['merges_file']      )
      with open(self.vocab_file		,     'w'		,     encoding='utf-8'      ) as fp:
        fp.write(json.dumps(__snake_case      ) + '\n'      )
      with open(self.merges_file		,     'w'		,     encoding='utf-8'      ) as fp:
        fp.write('\n'.join(__snake_case      )      )







    def      lowercase_			( self					:    Tuple		,     **__snake_case					:    Dict      ):
      kwargs.update(self.special_tokens_map      )
      return CodeGenTokenizer.from_pretrained(self.tmpdirname		,     **__snake_case      )







    def      lowercase_			( self					:    List[Any]		,     **__snake_case					:    List[str]      ):
      kwargs.update(self.special_tokens_map      )
      return CodeGenTokenizerFast.from_pretrained(self.tmpdirname		,     **__snake_case      )







    def      lowercase_			( self					:    Optional[int]		,     __snake_case					:    List[Any]      ):
      a		:   Tuple	  =	'lower newer'
      a		:   str	  =	'lower newer'
      return input_text, output_text







    def      lowercase_			( self					:    str      ):
      a		:   List[Any]	  =	CodeGenTokenizer(self.vocab_file		,     self.merges_file		,     **self.special_tokens_map      )
      a		:   Dict	  =	'lower newer'
      a		:   int	  =	['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
      a		:   List[str]	  =	tokenizer.tokenize(__snake_case		,     add_prefix_space=__snake_case      )
      self.assertListEqual(__snake_case		,     __snake_case      )

      a		:   Dict	  =	tokens + [tokenizer.unk_token]
      a		:   int	  =	[14, 15, 10, 9, 3, 2, 15, 19]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case      )		,     __snake_case      )







    def      lowercase_			( self					:    List[Any]      ):
      if not self.test_rust_tokenizer:
        return

      a		:   Optional[int]	  =	self.get_tokenizer()
      a		:   Tuple	  =	self.get_rust_tokenizer(add_prefix_space=__snake_case      )

      a		:   List[str]	  =	'lower newer'

      # Testing tokenization
      a		:   Optional[int]	  =	tokenizer.tokenize(__snake_case		,     add_prefix_space=__snake_case      )
      a		:   Optional[Any]	  =	rust_tokenizer.tokenize(__snake_case      )
      self.assertListEqual(__snake_case		,     __snake_case      )

      # Testing conversion to ids without special tokens
      a		:   Tuple	  =	tokenizer.encode(__snake_case		,     add_special_tokens=__snake_case		,     add_prefix_space=__snake_case      )
      a		:   str	  =	rust_tokenizer.encode(__snake_case		,     add_special_tokens=__snake_case      )
      self.assertListEqual(__snake_case		,     __snake_case      )

      # Testing conversion to ids with special tokens
      a		:   Optional[int]	  =	self.get_rust_tokenizer(add_prefix_space=__snake_case      )
      a		:   Optional[int]	  =	tokenizer.encode(__snake_case		,     add_prefix_space=__snake_case      )
      a		:   Dict	  =	rust_tokenizer.encode(__snake_case      )
      self.assertListEqual(__snake_case		,     __snake_case      )

      # Testing the unknown token
      a		:   Any	  =	tokens + [rust_tokenizer.unk_token]
      a		:   List[Any]	  =	[14, 15, 10, 9, 3, 2, 15, 19]
      self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__snake_case      )		,     __snake_case      )







    def      lowercase_			( self					:    List[str]		,     *__snake_case					:    int		,     **__snake_case					:    str      ):
      # It's very difficult to mix/test pretokenization with byte-level
      # And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
      pass







    def      lowercase_			( self					:    List[str]		,     __snake_case					:    str=15      ):
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""      ):
          a		:   List[Any]	  =	self.rust_tokenizer_class.from_pretrained(__snake_case		,     **__snake_case      )

          # Simple input
          a		:   Dict	  =	'This is a simple input'
          a		:   int	  =	['This is a simple input 1', 'This is a simple input 2']
          a		:   Dict	  =	('This is a simple input', 'This is a pair')
          a		:   Dict	  =	[
              ('This is a simple input 1', 'This is a simple input 2'),
              ('This is a simple pair 1', 'This is a simple pair 2'),
          ]

          # Simple input tests
          self.assertRaises(__snake_case		,     tokenizer_r.encode		,     __snake_case		,     max_length=__snake_case		,     padding='max_length'      )

          # Simple input
          self.assertRaises(__snake_case		,     tokenizer_r.encode_plus		,     __snake_case		,     max_length=__snake_case		,     padding='max_length'      )

          # Simple input
          self.assertRaises(
              __snake_case		,     tokenizer_r.batch_encode_plus		,     __snake_case		,     max_length=__snake_case		,     padding='max_length'		,     )

          # Pair input
          self.assertRaises(__snake_case		,     tokenizer_r.encode		,     __snake_case		,     max_length=__snake_case		,     padding='max_length'      )

          # Pair input
          self.assertRaises(__snake_case		,     tokenizer_r.encode_plus		,     __snake_case		,     max_length=__snake_case		,     padding='max_length'      )

          # Pair input
          self.assertRaises(
              __snake_case		,     tokenizer_r.batch_encode_plus		,     __snake_case		,     max_length=__snake_case		,     padding='max_length'		,     )







    def      lowercase_			( self					:    Optional[Any]      ):
      a		:   Any	  =	CodeGenTokenizer.from_pretrained(self.tmpdirname		,     pad_token='<pad>'      )

      # Simple input
      a		:   Union[str, Any]	  =	'This is a simple input'
      a		:   Dict	  =	['This is a simple input looooooooong', 'This is a simple input']
      a		:   int	  =	('This is a simple input', 'This is a pair')
      a		:   List[Any]	  =	[
          ('This is a simple input loooooong', 'This is a simple input'),
          ('This is a simple pair loooooong', 'This is a simple pair'),
      ]

      a		:   Optional[int]	  =	tokenizer.pad_token_id

      a		:   List[Any]	  =	tokenizer(__snake_case		,     padding='max_length'		,     max_length=30		,     return_tensors='np'      )
      a		:   Union[str, Any]	  =	tokenizer(__snake_case		,     padding=__snake_case		,     truncate=__snake_case		,     return_tensors='np'      )
      a		:   Optional[Any]	  =	tokenizer(*__snake_case		,     padding='max_length'		,     max_length=60		,     return_tensors='np'      )
      a		:   List[Any]	  =	tokenizer(__snake_case		,     padding=__snake_case		,     truncate=__snake_case		,     return_tensors='np'      )

      # s
      # test single string max_length padding
      self.assertEqual(out_s['input_ids'].shape[-1]		,     30      )
      self.assertTrue(pad_token_id in out_s['input_ids']      )
      self.assertTrue(0 in out_s['attention_mask']      )

      # s2
      # test automatic padding
      self.assertEqual(out_sa['input_ids'].shape[-1]		,     33      )
      # long slice doesn't have padding
      self.assertFalse(pad_token_id in out_sa['input_ids'][0]      )
      self.assertFalse(0 in out_sa['attention_mask'][0]      )
      # short slice does have padding
      self.assertTrue(pad_token_id in out_sa['input_ids'][1]      )
      self.assertTrue(0 in out_sa['attention_mask'][1]      )

      # p
      # test single pair max_length padding
      self.assertEqual(out_p['input_ids'].shape[-1]		,     60      )
      self.assertTrue(pad_token_id in out_p['input_ids']      )
      self.assertTrue(0 in out_p['attention_mask']      )

      # p2
      # test automatic padding pair
      self.assertEqual(out_pa['input_ids'].shape[-1]		,     52      )
      # long slice pair doesn't have padding
      self.assertFalse(pad_token_id in out_pa['input_ids'][0]      )
      self.assertFalse(0 in out_pa['attention_mask'][0]      )
      # short slice pair does have padding
      self.assertTrue(pad_token_id in out_pa['input_ids'][1]      )
      self.assertTrue(0 in out_pa['attention_mask'][1]      )







    def      lowercase_			( self					:    Optional[int]      ):
      a		:   int	  =	'$$$'
      a		:   Dict	  =	CodeGenTokenizer.from_pretrained(self.tmpdirname		,     bos_token=__snake_case		,     add_bos_token=__snake_case      )

      a		:   int	  =	'This is a simple input'
      a		:   Tuple	  =	['This is a simple input 1', 'This is a simple input 2']

      a		:   Any	  =	tokenizer.bos_token_id

      a		:   Union[str, Any]	  =	tokenizer(__snake_case      )
      a		:   Dict	  =	tokenizer(__snake_case      )

      self.assertEqual(out_s.input_ids[0]		,     __snake_case      )
      self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids      )      )

      a		:   Optional[Any]	  =	tokenizer.decode(out_s.input_ids      )
      a		:   Optional[Any]	  =	tokenizer.batch_decode(out_sa.input_ids      )

      self.assertEqual(decode_s.split()[0]		,     __snake_case      )
      self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa      )      )







    @slow
    def      lowercase_			( self					:    List[Any]      ):
      a		:   Tuple	  =	CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono'      )

      a		:   Union[str, Any]	  =	'\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#'
      a		:   Optional[Any]	  =	'\nif len_a > len_b:      result = a\nelse:      result = b'

      a		:   Any	  =	tokenizer.encode(__snake_case      )
      a		:   Union[str, Any]	  =	['^#', re.escape('<|endoftext|>'      ), '^\'\'\'', '^"""', '\n\n\n']
      a		:   List[str]	  =	tokenizer.decode(__snake_case		,     truncate_before_pattern=__snake_case      )
      self.assertEqual(__snake_case		,     __snake_case      )







    def      lowercase_			( self					:    Any      ):
      pass